Understanding ontological engineering
Communications of the ACM - Supporting community and building social capital
Hyperpatches for 3D Model Acquisition and Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Knowledge modeling -- State of the art
Integrated Computer-Aided Engineering
Artificial intelligence today: recent trends and developments
Artificial intelligence today: recent trends and developments
Brief Robust neural control for robotic manipulators
Automatica (Journal of IFAC)
| Hi-index | 0.01 |
The objective of expert systems is the use of Artificial Intelligence tools so as to solve problems within specific prefixed applications. In the last two decades a great experimental effort together with some theoretical knowledge have been employed to investigate the completeness and consistency of knowledge-based systems and to clarify the structure of these systems. Nevertheless, there is often a gap in the formalism which allows the structuring of the expert system programming towards the expert system design. In the last years, a new field called Ontological Engineering, defined by the IEEE as "the field that establishes a set of concepts, axioms, and relationships that describe a domain of scientific or technological interest" is trying to fill this gap. The work presented here may be placed in this context. In particular, the paper deals with the development of an expert system valid to optimize the adaptation transients arising in adaptive control using a logic formalism previously described, providing good simulation results. Its structure is composed by a supervisor based on an expert network organization and designed to improve the transient performances in the adaptive control of a planar robot. Apart form the basic adaptation scheme consisting of an estimation algorithm plus an adaptive controller, two additional coordinated expert systems are used to update an adaptation gain and the sampling period with a master expert system coordinating both above expert systems.